Measuring the Predictive Accuracy of the Correlated Gamma Ratio Method for Model Selection

Proceedings of the 5th Hellenic-European Conference on Computer Mathematics and its Applications, Athens, Greece, E.A. Lypitakis (ed.), pp.732-739, 2001

8 Pages Posted: 6 Feb 2008 Last revised: 23 Jan 2009

See all articles by Stavros Antonios Degiannakis

Stavros Antonios Degiannakis

Department of Economic and Regional Development, Panteion University of Political and Social Sciences

Evdokia Xekalaki

Athens University of Economics and Business

Abstract

Autoregressive Conditional Heteroscedasticity (ARCH) models have successfully been applied in order to predict asset return volatility. Predicting volatility is of great importance in pricing financial derivatives, selecting portfolios, measuring and managing investment risk more accurately. In this paper, a number of ARCH models are examined in the framework of a method for model selection based on the Correlated Gamma Ratio (CGR) distribution and their ability to predict future volatility is examined. According to this method, the ARCH model with the lowest sum of squared standardized forecasting errors is selected for predicting future volatility. A number of evaluation criteria are used to examine the performance of a model to predict future volatility, for forecasting horizons ranging from one day to one hundred days ahead. The results show that the CGR model selection procedure has a satisfactory performance in selecting that model that generates "better" volatility predictions. It appears, therefore, that it can be regarded as a tool in guiding one's choice of the appropriate model for predicting future volatility, with applications in evaluating portfolios, managing financial risk and creating speculative strategies with options.

Keywords: ARCH models, forecast volatility, model selection, predictability, correlated gamma ratio distribution

Suggested Citation

Degiannakis, Stavros Antonios and Xekalaki, Evdokia, Measuring the Predictive Accuracy of the Correlated Gamma Ratio Method for Model Selection. Proceedings of the 5th Hellenic-European Conference on Computer Mathematics and its Applications, Athens, Greece, E.A. Lypitakis (ed.), pp.732-739, 2001 , Available at SSRN: https://ssrn.com/abstract=1090284

Stavros Antonios Degiannakis

Department of Economic and Regional Development, Panteion University of Political and Social Sciences ( email )

136 Sygrou
Athens
Greece

Evdokia Xekalaki (Contact Author)

Athens University of Economics and Business ( email )

76 Patission Street
GR-10434 Athens
Greece

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